Computational Methods (OCR A Level Computer Science)

Revision Note

It's important to consider if problems can be solved using algorithms and programming code
For example:
- Sorting a list of numbers is feasible using algorithms like quick sort or merge sort
- But ethical problems or social problems, like determining if a loan should be approved, may incorporate some human input that algorithms could oversimplify or misinterpret
Problems that can be solved with algorithms and programming code are computational problems

Practical limitations such as computing power, speed, and memory can affect whether a problem is solvable
Running complex machine learning models on a regular laptop might be constrained by limited processing power and memory

Some problems are theoretically solvable but not practical due to resource limitations
For example, calculating Pi to a billion decimal places is theoretically possible but impractical due to the amount of computational resources needed

Technological improvements have expanded the types of problems that can be computationally solved
For example, genome sequencing has become quicker and more affordable due to advances in technology

Problem Description	Computational Problem (Yes or No)	Justification
Inventory levels are not updated in real-time	Yes	Real-time syncing can be achieved through algorithms
High rate of employee turnover	No	Root causes are likely cultural or managerial, not algorithmic
Incorrectly sorted products in the delivery van	Yes	Sorting algorithms can optimise placement for efficiency
Long wait times for customer service	Yes	Queue algorithms can improve response times
Poor route optimisation for delivery trucks	Yes	Routing algorithms exist for this specific problem
Inadequate marketing strategies	No	Marketing strategies often require creative and human-centric solutions

The table below outlines various challenges in a public transportation system.

Evaluate each problem and decide whether it is computational and justify each of your decisions.

Problem Description	Computational (Yes or No)	Justification
Frequent train delays
Overcrowding in peak hours
Difficulty in finding the shortest route
Vandalism in the stations
Inconsistent fare pricing
Inaccurate timetable

How to Answer This Question

Review the Table: Take a moment to read through the problems listed and think about whether a computational solution would be possible
Fill in the Table: In the "computational" column, specify whether the problem should or should not be solved computationally
Justify your answers: In the last column, justify your decision for each problem briefly

Answer:

Problem Description	Computational (Yes or No)	Justification
Frequent train delays	Yes	Real-time tracking and predictive algorithms can help in better scheduling
Overcrowding in peak hours	Yes	Load-balancing algorithms can redistribute passengers or add more trains during peak hours
Difficulty in finding the shortest route	Yes	Route-finding algorithms can quickly identify the most efficient path
Vandalism in the stations	No	Requires human intervention such as increased security personnel
Inconsistent fare pricing	Yes	Dynamic pricing algorithms can ensure fare consistency
Inaccurate timetable	Yes	Timetabling algorithms can generate more accurate and optimised timetables

to absolutely everything:

the (exam) results speak for themselves:

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